





"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:str						    =   ["""image_processor""", """tokenizer"""]
       __magic_name__	:List[str]						    =   """BridgeTowerImageProcessor"""
       __magic_name__	:Dict						    =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         super().__init__(__UpperCAmelCase       ,					__UpperCAmelCase )






       def __call__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 0       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.tokenizer(
             text=__UpperCAmelCase       ,					add_special_tokens=__UpperCAmelCase       ,					padding=__UpperCAmelCase       ,					truncation=__UpperCAmelCase       ,					max_length=__UpperCAmelCase       ,					stride=__UpperCAmelCase       ,					pad_to_multiple_of=__UpperCAmelCase       ,					return_token_type_ids=__UpperCAmelCase       ,					return_attention_mask=__UpperCAmelCase       ,					return_overflowing_tokens=__UpperCAmelCase       ,					return_special_tokens_mask=__UpperCAmelCase       ,					return_offsets_mapping=__UpperCAmelCase       ,					return_length=__UpperCAmelCase       ,					verbose=__UpperCAmelCase       ,					return_tensors=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)
         # add pixel_values + pixel_mask
         lowerCAmelCase__     :str											=				self.image_processor(
             __UpperCAmelCase       ,					return_tensors=__UpperCAmelCase       ,					do_normalize=__UpperCAmelCase       ,					do_center_crop=__UpperCAmelCase       ,					**__UpperCAmelCase )
         encoding.update(__UpperCAmelCase )

         return encoding






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return self.tokenizer.batch_decode(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return self.tokenizer.decode(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.tokenizer.model_input_names
         lowerCAmelCase__     :Optional[int]											=				self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )


"""simple docstring"""



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


a       :						int       		=		'''scheduler_config.json'''




class 		a_    (			_UpperCAmelCase						):
    a   :					List[Any]			     =					1
    a   :					Tuple			     =					2
    a   :					Dict			     =					3
    a   :					str			     =					4
    a   :					Optional[int]			     =					5
    a   :					Any			     =					6
    a   :					int			     =					7
    a   :					Any			     =					8
    a   :					List[Any]			     =					9
    a   :					Any			     =					10
    a   :					List[str]			     =					11
    a   :					Optional[int]			     =					12
    a   :					Any			     =					13
    a   :					Dict			     =					14







@dataclass
class 		a_    (			_UpperCAmelCase						):
    a   :					torch.FloatTensor







class 		a_    :
    a   :					Dict			     =					SCHEDULER_CONFIG_NAME
    a   :					List[str]			     =					[]
    a   :					Optional[int]			     =					True







    @classmethod
    def   _snake_case       (      cls      :							List[str]  ,       __UpperCamelCase      :							Dict[str, Any] = None  ,       __UpperCamelCase      :							Optional[str] = None  ,       __UpperCamelCase      :							List[str]=False  ,       **__UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = cls.load_config(
            pretrained_model_name_or_path=__UpperCamelCase  ,       subfolder=__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       return_commit_hash=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        return cls.from_config(__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Union[str, os.PathLike]  ,       __UpperCamelCase      :							bool = False  ,       **__UpperCamelCase      :							List[str]  )		->Any:






        '''simple docstring'''






        self.save_config(save_directory=__UpperCamelCase  ,       push_to_hub=__UpperCamelCase  ,       **__UpperCamelCase  )







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->List[Any]:






        '''simple docstring'''






        return self._get_compatibles()







    @classmethod
    def   _snake_case       (      cls      :							List[str]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = list(set([cls.__name__] + cls._compatibles  )  )
        _UpperCAmelCase        = importlib.import_module(__name__.split("""."""  )[0]  )
        _UpperCAmelCase        = [
            getattr(__UpperCamelCase  ,       __UpperCamelCase  ) for c in compatible_classes_str if hasattr(__UpperCamelCase  ,       __UpperCamelCase  )
        ]
        return compatible_classes